How Data Teams Master Causal Inference for Better Decisions
transcript
show notes
Most data teams treat correlation as causation, leading to expensive marketing missteps and product features nobody uses. In this episode, Lucas and Luna drill into causal inference — specifically the use of propensity score matching and synthetic control methods — to separate signal from noise. They examine a real-world case where a major e-commerce platform used these techniques to discover that their flash sales were cannibalizing regular revenue rather than generating new demand. With specific examples from healthcare analytics and tech A-B testing, we show how to move beyond simple regression to understand what actually drives outcomes, helping you make decisions based on true impact rather than statistical coincidence.
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